The present invention relates to a reliability acquiring apparatus, a reliability acquiring method, and a reliability acquiring program.
A large number of detection techniques for specifying a position of a target (e.g., a face region) in an image have been proposed. In most of such detection techniques, a target is detected when an input image is given, by thoroughly searching through the input image using a classifier that determines whether an image region is the target.
The classifier used in face detection is usually generated by preparing a large quantity of cropped images of face regions and images not including a face and performing learning. However, it is difficult to prepare an image group for the learning completely including information necessary for discriminating whether a face region is a face. Therefore, in general, a built classifier involves a certain degree of detection errors. The detection errors include two kinds of detection errors: a face region cannot be determined as the face and is overlooked (un-detection); and a region that is not the face is determined as the face by mistake (misdetection). Concerning the latter, several techniques for reducing the number of misdetections are known.
For example, Patent Document 1 describes, as the method of reducing misdetections of a face, a method of determining whether a region detected as a face is the face according to whether a color of the region is a skin color.
Patent Document 2 describes, as the method of reducing misdetections of a face, a method of determining whether a face region is a face using a statistical model concerning textures and shapes of faces. In this method, parameters of the statistical model is adjusted and a difference in intensity values between a face image generated from the model and an cropped image of a face region on the basis of a face detection result is minimized. Note that the face image generated from the model and the cropped image of the face region on the basis of the face detection result are respectively normalized concerning a face shape. When the minimized difference in the intensity values is equal to or larger than a predetermined threshold, it is determined that a detection result is a misdetection. Usually, the statistical model concerning the face has poor expression of images other than the face, if the cropped face image on the basis of the face detection result is not the face (misdetection), the difference in the intensity values between the face image generated from the model and the cropped face image on the basis of the face detection result is considered to increase. The method described in Patent Document 2 is a method of determining on the basis of such knowledge whether the face detection result is truly the face according to the difference in the intensity values.
Non-Patent Document 1 proposes, as the method of reducing misdetections of a face, a method of learning a face misdetection determination device using a support vector machine (SVM) and applying the determination device to a region cropped on the basis of a face detection result to more actively eliminate a misdetection. In this method, a classifier is built that learns an image feature value extracted by Gabor wavelet transformation using the SVM and identifies whether a texture of a target region is like a face.
Patent Document 1: Patent Publication JP-A-2009-123081
Patent Document 2: Patent Publication JP-A-2010-191592
Non-Patent Document 1: Yamashita, et al., “False Detection Reduction for Face Detection”, The Institute of Electronics, Information and Communication Engineers Technical Research Report PRMU2004-102
Non-Patent Document 2: D. Cristinacce and T. F. Cootes, “A Comparison of Shape Constrained Facial Feature Detectors,” In 6th International Conference on Automatic Face and Gesture Recognition 2004, Korea, pages 357-380, 2004